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Detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model

Functional brain networks exhibit modular community structure with highly inter-connected nodes within a same module, but sparsely connected between different modules. Recent neuroimaging studies also suggest dynamic changes in brain connectivity over time. We propose a dynamic stochastic block mode...

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Main Authors: Samdin, S. Balqis, Ting, Chee-Ming, Ombao, Hernando
Format: Conference or Workshop Item
Published: 2019
Subjects:
QM Human anatomy
Online Access:http://eprints.utm.my/id/eprint/97111/
http://dx.doi.org/10.1109/ISBI.2019.8759405
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id my.utm.97111
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spelling my.utm.971112022-09-23T01:25:23Z http://eprints.utm.my/id/eprint/97111/ Detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model Samdin, S. Balqis Ting, Chee-Ming Ombao, Hernando QM Human anatomy Functional brain networks exhibit modular community structure with highly inter-connected nodes within a same module, but sparsely connected between different modules. Recent neuroimaging studies also suggest dynamic changes in brain connectivity over time. We propose a dynamic stochastic block model (SBM) to characterize changes in community structure of the brain networks inferred from neuroimaging data. We develop a Markov-switching SBM (MS-SBM) which is a non-stationary extension combining time-varying SBMs with a Markov process to allow for state-driven evolution of the network community structure. The time-varying connectivity parameters within and between communities are estimated from dynamic networks based on sliding-window approach, assuming a constant community membership of nodes recovered by using spectral clustering. We then partition the time-evolving community structure into recurring, piecewise constant regimes or states using a hidden Markov model. Simulation shows that the proposed MS-SBM gives accurate tracking of dynamic community regimes. Application to a task-evoked fMRI data reveals dynamic reconfiguration of the brain network modular structure in language processing between alternating blocks of story and math tasks. 2019 Conference or Workshop Item PeerReviewed Samdin, S. Balqis and Ting, Chee-Ming and Ombao, Hernando (2019) Detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model. In: 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, 8 - 11 April 2019, Venice, Italy. http://dx.doi.org/10.1109/ISBI.2019.8759405
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QM Human anatomy
spellingShingle QM Human anatomy
Samdin, S. Balqis
Ting, Chee-Ming
Ombao, Hernando
Detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model
description Functional brain networks exhibit modular community structure with highly inter-connected nodes within a same module, but sparsely connected between different modules. Recent neuroimaging studies also suggest dynamic changes in brain connectivity over time. We propose a dynamic stochastic block model (SBM) to characterize changes in community structure of the brain networks inferred from neuroimaging data. We develop a Markov-switching SBM (MS-SBM) which is a non-stationary extension combining time-varying SBMs with a Markov process to allow for state-driven evolution of the network community structure. The time-varying connectivity parameters within and between communities are estimated from dynamic networks based on sliding-window approach, assuming a constant community membership of nodes recovered by using spectral clustering. We then partition the time-evolving community structure into recurring, piecewise constant regimes or states using a hidden Markov model. Simulation shows that the proposed MS-SBM gives accurate tracking of dynamic community regimes. Application to a task-evoked fMRI data reveals dynamic reconfiguration of the brain network modular structure in language processing between alternating blocks of story and math tasks.
format Conference or Workshop Item
author Samdin, S. Balqis
Ting, Chee-Ming
Ombao, Hernando
author_facet Samdin, S. Balqis
Ting, Chee-Ming
Ombao, Hernando
author_sort Samdin, S. Balqis
title Detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model
title_short Detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model
title_full Detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model
title_fullStr Detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model
title_full_unstemmed Detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model
title_sort detecting state changes in community structure of functional brain networks using a markov-switching stochastic block model
publishDate 2019
url http://eprints.utm.my/id/eprint/97111/
http://dx.doi.org/10.1109/ISBI.2019.8759405
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score 13.251813

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